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PhD, eligible candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated
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discount programs Position Status Full Time Working Hours Standard Hours 37.50 Daily Work Shift Work Arrangement This position requires a fully on-site work arrangement. Union Description Post Docs - Regular
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candidates for research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning
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Movement Sciences at Rutgers University is seeking a highly motivated Post Doctoral Associate to work on translational projects at the intersection of biomechanics, machine learning, exergaming, and mobile
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
Position Title Post-Doc Research Associate Vacancy ID PDS004749 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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Full Time Posting Number 26FA0157 Posting Open Date Posting Close Date 08/31/2026 Qualifications Minimum Education and Experience PhD, MD, or equivalent doctoral degree in neuroscience, biostatistics
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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning
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invite applications for a Post Doc position at the 3RTG: Graduate School: Al based assessment of behaviour and thermal changes in veterinary infectious disease models (Project P08: Prof. Thöne-Reineke
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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silico identification of candidate developmental pathways explaining tradeoff variation. Contribute to advanced statistical analyses and interpretable machine learning approaches (in collaboration with